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MINING AND AGGREGATION OF CULTURAL DATA IN SOCIAL NETWORKS

Year 2017, , 122 - 131, 30.04.2017
https://doi.org/10.18769/ijasos.309502

Abstract

The explosive growth of the
Internet, the emergence of social networks and recent technological advances
enabled an enormous user population to become actuators in this new emerging
cultural environment. Handheld wireless devices, like smartphones and tablets,
which can be internet-connected, allow users to join the Internet community
from any place at any time. Users are of various and diverse cultural profiles.
Social networks form a modern global environment where all these users can
actually become cultural actuators in the sense that they socialize,
communicate, announce and reproduce information promoting local, national and
international activities closely related to their cultural background.



Modern social networks, like
Facebook or Twitter, form active and vivid channels of cultural information
circulation. Thousands of single users or user groups make frequent
announcements about special cultural events, related to music, dance, theater,
cinema, gastronomy, performances, exhibitions, gatherings of a special cultural
character. In addition, such announcements made in the form of short, inclusive
posts bear unique online features so that their audience can immediately
exploit them. However, it remains an important challenge to efficiently mine
useful data from such populated, diverse and vaguely structured spaces.



Motivated by the case of Santorini
Island, Greece and a strong recent observation that local traditional
activities or special (multi-)cultural events and activities tend to be absent
from touristic guides and plans, we present a WordPress-based website which
automatically collects cultural data from Facebook and presents it in a
comprehensive way for promoting cultural activity in Santorini.



Lack of information implies lack of
knowledge which consequently results in a reduced interest and decision space.
Utilizing keywords spanning a variety of cultural activities and events, our
system serves as an aggregator for Facebook posts of particular cultural
interest. While several, mainly not collaborating, entities – like for instance
Facebook users or groups, websites, Twitter users or groups - do release this
sort of information, lack of organization and timely viewing makes it extremely
inefficient for interested entities to locate, evaluate and exploit this highly
distributed and unstructured material.



The experimental use of our system so far – as an application offered
from the Department of Cultural Heritage Management and New Technologies of the
University of Patras – shows that technology can
indeed serve an important role towards efficient cultural management and
fruitful intercultural cooperation.

References

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Year 2017, , 122 - 131, 30.04.2017
https://doi.org/10.18769/ijasos.309502

Abstract

References

  • Aiken M., Balan S. (2011). An Analysis of Google Translate Accuracy. Translation Journal, vol. 16, Issue 2. Aiken, M., Ghosh, K., Wee, J., Vanjani, M. (2009). An Evaluation of the Accuracy of Online Translation Systems. Communications of the IIMA, vol. 9, Issue 4, pp. 67-79. BuiltWith (2016). CMS Usage Statistics: Statistics for websites using CMS technologies. https://trends.builtwith.com/cms Carlson, N. (2010). At Last—The Full Story of How Facebook Was Founded. Business Insider. Corbin B. (2010). WordPress Top Plugins. Packt Publishing. ISBN-13: 978-1849511407 Easley D., Kleinberg J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press. ISBN-10: 0521195330. Furedi, F. (2014). How The Internet and Social Media Are Changing Culture. Aspen Review, no. 4. Aspen Institute Prague. Kidd, T. T. (2008). Social Information Technology: Connecting Society and Cultural Issues. Information Science Reference, IGI Global. ISBN 978-1-59904-774-4. Phillips, S. (2007). A brief history of Facebook. The Guardian. Sawyer, R. (2011). The Impact of New Social Media on Intercultural Adaptation. DigitalCommons@URI. Shankland S. (2013). Google Translate now serves 200 million people daily. CNET. Starr, J., Coyier, C. (2009). Digging Into WordPress. Self Published. https://digwp.com/book/ von Ahn, L., Blum, M., Hopper, N. J., Langford, J. (2003). CAPTCHA: Using Hard AI Problems for Security. In Proceedings of the 2003 International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2003), pp. 294-311. W3Techs Web Technology Surveys (2016). Usage Statistics and Market Share of Content Management Systems for Websites. https://w3techs.com/techfeed/survey/content_management Wasserman, S., Faust, K. (1994). Social Network Analysis in the Social and Behavioral Sciences. Social Network Analysis: Methods and Applications, pp. 1–27, Cambridge University Press. ISBN 9780521387071.
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Details

Journal Section Articles
Authors

Evi Papaioannou

Elpida Schiza

Publication Date April 30, 2017
Submission Date April 27, 2017
Published in Issue Year 2017

Cite

EndNote Papaioannou E, Schiza E (April 1, 2017) MINING AND AGGREGATION OF CULTURAL DATA IN SOCIAL NETWORKS. IJASOS- International E-journal of Advances in Social Sciences 3 7 122–131.

Contact: ijasosjournal@hotmail.com

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